Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A non-invasive device for monitoring physiological conditions, the device comprising: a set of electrodes (leads) is disposed around, on, or within a human body in a manner to produce a desired differential voltage across them representing the voltage gradient generated by the electrical properties of the cardiac tissue; and a sensing device connected to the set of electrodes which is either implanted into the human body or resides external to the human body, that comprising a microprocessor unit which determines whether to activate an alarm that the monitored person's health is at risk; wherein microprocessor unit performing a linear prediction (LP) analysis on the digitized signal to obtain the coefficients of the synthesis filter of the CELP model for the digitalized signal using the autocorrelation approach, the autocorrelation of windowed quasi-periodic waveform is converted to the LP coefficients using the Levinson-Durbin algorithm; wherein the LP coefficients of the synthesis filter through a Haar discreet wavelet transformation is converted to the Line Spectral Frequencies (LSFs) form data associated with shot-term prediction and excitation; and wherein microprocessor unit reduces the size of the information containing in the plurality of data structures based on Chebyshev polynomial evaluation method.
2. The device of claim 1 , wherein the n-level filter bank gets the LP coefficients decomposed into low and high frequencies, due to the process of decomposition, the number of the LP coefficients must be a multiple of 2n where n is the number of levels.
This invention relates to signal processing, specifically a multi-level filter bank system for decomposing linear prediction (LP) coefficients into frequency components. The problem addressed is efficiently separating LP coefficients into low and high frequency bands while ensuring the coefficient count aligns with the filter bank's hierarchical structure. The system includes an n-level filter bank that processes LP coefficients by decomposing them into low and high frequency components. The decomposition process requires the number of LP coefficients to be a multiple of 2n, where n is the number of decomposition levels. This constraint ensures proper frequency separation across all levels. The filter bank may use quadrature mirror filters or similar techniques to split the coefficients into subbands. Each level of decomposition further divides the frequency components, allowing for detailed frequency analysis or synthesis. The system may be used in applications like audio processing, speech coding, or signal compression, where frequency-domain analysis of LP coefficients is beneficial. The invention ensures efficient decomposition while maintaining mathematical consistency in the coefficient count.
3. The device of claim 1 , wherein the microprocessor unit executes a classification algorithm in order to determine evaluates the clinical and biochemical indicators in blood of a mammal, especially a human.
This invention relates to a medical device for analyzing clinical and biochemical indicators in blood, particularly for human patients. The device includes a microprocessor unit that executes a classification algorithm to evaluate these indicators. The primary function is to process blood sample data to assess health conditions or biochemical states. The classification algorithm likely involves machine learning or statistical methods to interpret the blood parameters, which may include markers for diseases, metabolic conditions, or other physiological states. The device may integrate with blood collection systems, such as sensors or analyzers, to input raw or preprocessed data for evaluation. The microprocessor unit could also interface with a display or output system to present results, such as diagnostic classifications or risk assessments. The invention aims to provide automated, accurate, and rapid analysis of blood indicators, improving clinical decision-making or patient monitoring. The classification algorithm may be trained on historical data to enhance accuracy in identifying patterns or anomalies in the blood parameters. The device could be used in hospitals, clinics, or point-of-care settings for real-time or batch processing of blood samples.
4. A non-invasive system for monitoring physiological conditions, the system comprising: the device of claim 1 ; wherein the microprocessor unit is not mounted to the sensing device.
A non-invasive system monitors physiological conditions by detecting and analyzing bioelectrical signals from a subject. The system includes a sensing device that captures bioelectrical signals, such as those generated by muscle or nerve activity, and a separate microprocessor unit that processes these signals to derive physiological information. The sensing device is designed to be placed on the subject's body without invasive procedures, ensuring comfort and ease of use. The microprocessor unit, which is not physically mounted to the sensing device, receives the captured signals and performs signal processing, such as filtering, amplification, and analysis, to extract relevant physiological parameters. These parameters may include muscle activity, nerve function, or other bioelectrical characteristics. The system may also include additional components, such as a display or communication interface, to present the processed data to a user or transmit it to another device for further analysis. The separation of the sensing device and microprocessor unit allows for flexibility in system design, enabling the sensing device to be lightweight and portable while the microprocessor unit can be housed in a separate, more robust unit. This configuration is particularly useful in medical, fitness, or rehabilitation applications where continuous, non-invasive monitoring of physiological conditions is required.
5. The system of claim 4 , wherein data related to quasi-periodic waveforms (Data) are stored in a data base of data center, this data center as input for a computer program utilizing the above-described method, data structure, and/or program contained on a tablet device, and executed by a central processing unit.
This invention relates to a system for processing and analyzing quasi-periodic waveforms, such as those found in biomedical signals, financial time series, or other cyclical data. The system addresses the challenge of efficiently storing, retrieving, and analyzing such waveforms, which often exhibit irregular periodicities that complicate traditional signal processing techniques. The system includes a data center that stores data related to quasi-periodic waveforms in a database. This stored data serves as input for a computer program executed by a central processing unit (CPU) on a tablet device. The program utilizes a specific method, data structure, and/or algorithm designed to handle the unique characteristics of quasi-periodic signals. The method involves analyzing the waveforms to extract meaningful patterns, trends, or anomalies, which may be used for monitoring, prediction, or decision-making purposes. The data structure ensures efficient storage and retrieval of the waveform data, while the algorithm optimizes the processing of these signals to account for their irregular periodicities. The system leverages the computational power of the tablet device to perform real-time or near-real-time analysis, enabling applications such as health monitoring, financial forecasting, or industrial process control. The integration of the data center with the tablet device allows for centralized data management while maintaining the flexibility and portability of a mobile platform. This approach enhances the usability and scalability of the system, making it suitable for a wide range of applications where quasi-periodic waveform analysis is required.
6. The system of claim 4 , wherein a Human-Computer Interface (HCl) by which an operator (e.g., a physician, technician, or patient) may interact with the MPU and which may include a visual display, auditory display, keyboard, touch screen, haptic device, or other means of interaction.
This invention relates to a medical system designed to enhance human-computer interaction in healthcare applications. The system includes a main processing unit (MPU) that performs medical data analysis, such as processing physiological signals, medical imaging, or patient records. The MPU is configured to generate outputs based on this analysis, which may include diagnostic results, treatment recommendations, or alerts. A key feature of the system is its Human-Computer Interface (HCI), which allows operators—such as physicians, technicians, or patients—to interact with the MPU. The HCI supports multiple input and output modalities, including visual displays, auditory feedback, keyboards, touch screens, and haptic devices. This flexibility ensures that the system can accommodate different user needs and environments, such as clinical settings, remote monitoring, or patient self-management. The HCI is designed to facilitate efficient and intuitive interaction with the MPU, enabling users to input commands, review data, and receive feedback in real time. For example, a physician might use a touch screen to navigate medical images, while a patient could receive auditory alerts via a mobile device. The system may also incorporate adaptive interfaces that adjust based on user preferences or contextual factors, improving usability and accessibility. By integrating these interactive elements, the system aims to streamline medical workflows, reduce errors, and enhance patient engagement. The HCI's modular design allows for future expansion, supporting additional interaction methods as technology evolves.
Unknown
January 14, 2020
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